The spectral energy distribution of the dark cloud LDN 1622, as measured by Finkbeiner using WMAP data, drops above 30 GHz and is suggestive of a Boltzmann cutoff in grain rotation frequencies, characteristic of spinning dust emission. LDN 1622 is conspicuous in the 31 GHz image we obtained with the Cosmic Background Imager, which is the first centimeter-wave resolved image of a dark cloud. The 31 GHz emission follows the emission traced by the four IRAS bands. The normalized cross-correlation of the 31 GHz image with the IRAS images is higher by 6.6 for the 12 and 25 m bands than for the 60 and 100 m bands: C 12þ25 ¼ 0:76 AE 0:02, and C 60þ100 ¼ 0:64 AE 0:01. The mid-IR-centimeter-wave correlation in LDN 1622 is evidence for very small grain (VSG) or continuum emission at 26-36 GHz from a hot molecular phase. In dark clouds and their photon-dominated regions (PDRs), the 12 and 25 m emission is attributed to stochastic heating of the VSGs. The mid-IR and centimeter-wave dust emissions arise in a limb-brightened shell coincident with the PDR of LDN 1622, where the incident UV radiation from the Ori OB 1b association heats and charges the grains, as is required for spinning dust.

We present the first results of the High Cadence Transient Survey (HiTS), a survey for which the objective is to detect and follow-up optical transients with characteristic timescales from hours to days, especially the earliest hours of supernova (SN) explosions. HiTS uses the Dark Energy Camera and a custom pipeline for image subtraction, candidate filtering and candidate visualization, which runs in real-time to be able to react rapidly to the new transients. We discuss the survey design, the technical challenges associated with the real-time analysis of these large volumes of data and our first results. In our 2013, 2014, and 2015 campaigns, we detected more than 120 young SN candidates, but we did not find a clear signature from the short-lived SN shock breakouts (SBOs) originating after the core collapse of red supergiant stars, which was the initial science aim of this survey. Using the empirical distribution of limiting magnitudes from our observational campaigns, we measured the expected recovery fraction of randomly injected SN light curves, which included SBO optical peaks produced with models from Tominaga et al. (2011) andSari (2010). From this analysis, we cannot rule out the models from Tominaga et al. (2011) under any reasonable distributions of progenitor masses, but we can marginally rule out the brighter and longer-lived SBO models from Nakar & Sari (2010) under our best-guess distribution of progenitor masses. Finally, we highlight the implications of this work for future massive data sets produced by astronomical observatories,such as LSST.

The diffuse cm wave IR-correlated signal, the 'anomalous' CMB foreground, is thought to arise in the dust in cirrus clouds. We present Cosmic Background Imager (CBI) cm wave data of two translucent clouds, ζ Oph and LDN 1780 with the aim of characterizing the anomalous emission in the translucent cloud environment.In ζ Oph, the measured brightness at 31 GHz is 2.4σ higher than an extrapolation from 5-GHz measurements assuming a free-free spectrum on 8 arcmin scales. The SED of this cloud on angular scales of 1• is dominated by free-free emission in the cm range. In LDN 1780 we detected a 3σ excess in the SED on angular scales of 1• that can be fitted using a spinning dust model. In this cloud, there is a spatial correlation between the CBI data and IR images, which trace dust. The correlation is better with near-IR templates (IRAS 12 and 25 µm) than with IRAS 100 µm, which suggests a very small grain origin for the emission at 31 GHz.We calculated the 31-GHz emissivities in both clouds. They are similar and have intermediate values between that of cirrus clouds and dark clouds. Nevertheless, we found an indication of an inverse relationship between emissivity and column density, which further supports the VSGs origin for the cm emission since the proportion of big relative to small grains is smaller in diffuse clouds.

We present a Bayesian Voronoi image reconstruction technique (VIR) for interferometric data. Bayesian analysis applied to the inverse problem allows us to derive the a-posteriori probability of a novel parameterization of interferometric images. We use a variable Voronoi diagram as our model in place of the usual fixed pixel grid. A quantization of the intensity field allows us to calculate the likelihood function and a-priori probabilities. The Voronoi image is optimized including the number of polygons as free parameters. We apply our algorithm to deconvolve simulated interferometric data. Residuals, restored images and chi^2 values are used to compare our reconstructions with fixed grid models. VIR has the advantage of modeling the image with few parameters, obtaining a better image from a Bayesian point of view.Comment: 27 pages, 10 figures, to be published in APJ, 672, 127